Cognitive Processes Exam 3

category

the set of all individual things in the world

Ex: birds - robins, crows, penguins, eagles, etc.

why categories are useful

They help structure thought, memory, and perception; They help to understand individual cases not previously encountered; They provide cognitive economy

Family resemblance

when an object’s characteristics have a large amount of overlap with those of many other objects in that category

Cognitive economy

Being in the same category emphasizes similarity; Being in a different category emphasizes distinctiveness; Dividing the world into categories involves tradeoffs between similarities and differences

hierarchical organization of categories

vertical (level of abstraction) and horizontal

Level of abstraction

superordinate level, basic level, subordinate level

Superordinate level

relatively general

ex: vehicle

basic level

important level, greatest gain in information

ex: car

Subordinate level

highly specific

ex: toyota camry

Rosch et al

Participants listed attributes for categories at each level

Basic level: most cognitively economical

Moving from the superordinate level to the basic level

a significant increase in cognitive efficiency

Moving from the basic level to the subordinate level

shift from general categories to more specific classification

Why basic level categories are “special”

They provide cognitive economy (the greatest gain in information); fastest to name; strongest priming effects; learned early by children; short common words

what varies along the horizontal dimension of hierarchical organization

typicality

typicality

People are faster to classify typical instances as members of a category than atypical instances

They are cognitively prioritized and used as reference points for understanding categories

ex: birds; “robin” would be named first

different views of categories

classical view, probabilistic view, prototype view, exemplar view

classical view

Membership is determined based on whether the object meets the definition of the category

problems: defining features of concepts is hard, disjunctive concepts, goals and context influence categorization, doesn’t capture common sense

probabilistic view

Membership is determined by an item being classified based on its similarity to a category

accounts for typicality effects by proposing that things are “better” members if their weighted sum is higher

Limitations: does not account well for context effects, some categories are too ad hoc

prototype view

Membership is established by comparison with a prototype

Accounts for typicality effects by proposing that categories are organized around a “best example.”

limitations: does not capture category boundaries, ignores variability, struggles with abstract concepts

prototype

average representation of the “typical” member of a category

exemplar view

Membership is established by comparing a new object or encounter to specific, stored memories of previously experienced instances of a category

Accounts for typicality effects through similarity and frequency

Accounts for atypical cases because it does not discard “outlier” data

Accounts for ad hoc categories through the focus on context and goal-directed retrieval

approaches to the organization of conceptual knowledge that involve networks

the hierarchical semantic network and connectionist models

approaches to the organization of conceptual knowledge that do not involve networks

the feature comparison model, scripts and schemas, perceptual symbols and the embodied approach

what the embodied approach proposes about how conceptual knowledge is represented in the brain

knowledge of concepts is based on the reactivation of sensory and motor processes that occur when we interact with the object

How mirror neurons provide evidence for the embodied view of conceptual understanding

shows that the same neural circuits are active when performing an action and observing it

How semantic somatotopy provides evidence for the embodied view of conceptual understanding

demonstrates that processing language about actions automatically activates the same motor cortex regions used to execute those actions

Hauk, Johnsrufe, & Pulvermuller

Reading action words (e.g., kick, pick, and lick) activates motor and premotor areas corresponding to leg, arm, and face movements

How perceptual symbols differ from amodal or propositional representations

concepts are multimodal and involve perceptuo-motor representations (Barsalou, 1999)

What spatial congruity effects suggest about how information is represented and accessed in the mind

We are faster to judge pairs of words (e.g., CUP-SAUCER) as semantically related when their configuration matches their canonical relative position in the world (Zwaan & Yaxley, 2003)

What modality switching costs suggest about how information is represented and accessed in the mind

We are slower to evaluate properties of objects when having to switch modalities (Pecher, Zeelenberg, & Barsalou, 2003)

e.g., LEMON-SOUR, followed by TOMATO-RED vs. STRAWBERRY-SWEET

limitations of the perceptual symbols model

not clear how these perceptual representations are organized

possible solution: a hybrid system in which perceptual information is linked to concepts in a semantic network

properties of semantic networks

nodes and links

node

depicts a concept

link

depicts a relation between nodes.

distance between nodes

depicts a degree of association or similarity (and determines RT)

spreading activation

When a node is activated, activity spreads out along all connected links

concepts that receive activation are primed and more easily accessed from memory

priming

exposure to a stimulus unconsciously influences responses to future stimuli

ways we can study priming

lexical decision task and Meyer and Schvaneveldt (1971)

lexical decision task

participants read stimuli and are asked to say as quickly as possible whether the item is a word or not

Meyer and Schvaneveldt (1971)

found that reaction time was faster for closely associated pairs

e.g., fast: BREAD-BUTTER, slow: DOCTOR-BUTTER, BREAD-MARB

Hierarchical semantic network

has nodes and links, properties are stored with nodes, and additional properties can be determined by moving up and down the network, exceptions are stored at lower level nodes

It has cognitive economy: shared properties are only stored at higher-level nodes

Implication: it takes time to “move” from one level to another. Additional time is required to retrieve the features stored at one of the levels

advantages of the hierarchical semantic network

It predicts response times in sentence verification tasks and explains some priming/facilitation effects

sentence verification task

Participants determine if a presented sentence is true or false

slower to confirm that “a canary is an animal” than “a canary is a bird” - more links must be searched

slower to confirm “a canary has skin” than “a canary can fly” - the mental search must ravel from canary up to bird, then up to animal

limitations of the hierarchical semantic network model

It cannot explain typicality effects, and it cannot explain reversals of category size effects

why the hierarchical semantic network model cannot explain typicality effects

It predicts response times based solely on node distance in a strict hierarchy, not how representative an item is of a category

“A canary is a bird” and “an ostrich is a bird” will have equally fast reaction times